Data Engineer

• Own and improve the reporting and semantic layer in DOMO.
• Integrate data from Snowflake, BigQuery, SQL Server, and other external sources into DOMO.
• Build and optimize DOMO data flows, datasets, and reporting pipelines.
• Administer and maintain Snowflake environments, including provisioning, RBAC, security controls, and access management.
• Design and operate scalable Snowflake ingestion and transformation pipelines using Streams and Dynamic Tables.
• Manage BigQuery integrations, access controls, and connectivity with DOMO.
• Design, troubleshoot, and optimize complex SQL Server queries and database workflows.
• Use advanced SQL techniques, including window functions, to support data transformation and analysis.
• Build and maintain orchestration pipelines using Airflow, Dagster, or similar tools.
• Develop and maintain dbt models, testing, documentation, and transformation workflows.
• Monitor data quality, pipeline performance, and reliability across the end-to-end data lifecycle.
• Partner with engineering, analytics, and business teams to define data requirements and deliver dependable solutions.

Senior Data Engineer

• Lead the design, development, and optimization of ETL and data pipelines across multiple platforms, ensuring reliability, scalability, and performance.
• Define and implement best practices for data integration, transformation, quality, security, and governance.
• Manage data migrations, archival processes, and integrations across various systems and databases.
• Create and maintain advanced data visualizations, dashboards, and analytical models (e.g., Power BI or equivalent) to support business insights and decision-making.
• Collaborate with cross-functional teams to define data requirements, translate business needs into technical solutions, and deliver high-quality outcomes.
• Implement and configure platform components and new features for both new and existing clients.
• Set up and maintain unified data models across disparate data sources, ensuring consistency and accessibility.
• Regularly engage with customers to gather requirements, scope and prioritize incoming technical requests, and provide hands-on implementation support.
• Troubleshoot and resolve technical issues, continuously improving system performance and reliability.
• Develop sustainable and automated solutions to address recurring issues, contributing to internal tools and process optimization.
• Maintain detailed documentation and build internal knowledge bases for configurations, workflows, and best practices.
• Mentor and support team members, fostering technical growth and adherence to best practices.
• Collaborate with Product and Engineering teams to provide actionable feedback that enhances the platform and customer experience.
• Promote and uphold Boldr’s values by supporting cross-team collaboration, contributing to a positive culture, and operating with integrity and purpose.

• Design, develop, maintain, and optimize data pipelines that support data integration, transformation, and analytics initiatives
• Develop and maintain data models, data warehouses, and data structures that support reporting, business intelligence, and enterprise analytics
• Extract, transform, validate, and load data from multiple source systems while ensuring data quality, integrity, consistency, and compliance with business requirements
• Support the design, implementation, maintenance, and optimization of the organization’s data architecture and technology platforms
• Develop, maintain, and optimize SQL queries, database objects, and data transformation processes to improve performance and scalability
• Collaborate with Business Intelligence, Analytics, Technology, and business stakeholders to gather requirements and deliver data solutions that support business objectives
• Support data quality initiatives by identifying, troubleshooting, and resolving data inconsistencies, duplicate records, and integration issues
• Prepare and maintain technical documentation, data dictionaries, process documentation, and knowledge base resources to support ongoing operations and knowledge transfer
• Communicate technical concepts, project updates, and data insights effectively to technical and non-technical stakeholders
• Research and evaluate emerging technologies, tools, and best practices to improve data engineering capabilities and operational efficiency
• Adhere to company policies and procedures
• Meet or exceed performance targets for related KPIs
• Continuously improve knowledge of products, services, and processes by participating in training programs and continuous learning modules
• Collaborate with other departments as needed
• Maintain a positive, empathetic, and professional attitude toward customers and colleagues at all times

• Build and maintain scalable data pipelines for structured (CSV) data ingestion
• Build and optimize pipelines for data lakes and warehouses (Azure Data Lake, Synapse)
• Develop and maintain REST API integrations and system connectors
• Implement data validation, integrity, and deduplication processes
• Work closely with engineers to align data & application architecture
• Help shape cloud and data architecture decisions

• design, build, and scale modern cloud-based data platforms
• lead the architecture and operational management of the Snowflake data warehouse platform
• develop robust ELT/ETL pipelines to deliver high-quality data
• manage Snowflake access controls, roles, permissions, and secure data sharing practices

Senior Data Engineer, Snowflake

• Collaborating with internal auditors and engineering teams to understand audit workflows, data sources, and key challenges
• Developing AI and machine learning prototypes for anomaly detection, risk scoring, and data classification
• Building automation tools that streamline audit procedures and improve operational efficiency
• Supporting model evaluation, validation, and tuning to enhance accuracy and reliability
• Assisting in integrating AI components into internal tools and dashboards used by audit teams
• Documenting methodologies, models, and technical outputs to enable knowledge sharing and reuse

AI and Data Engineering Intern

• Design, develop, and optimize data pipelines for both batch and streaming workloads using AWS + Databricks.
• Implement data models, transformations, and ingestion frameworks following best practices (e.g., Medallion architecture: Bronze–Silver–Gold)
• Manage and maintain ETL/ELT processes using Azure Data Factory, Fabric Pipelines, or equivalent tools.
• Develop and maintain high-performance SQL queries and analytical models for reporting and analytics.
• Ensure data quality, reliability, and lineage through testing, monitoring, and documentation.
• Collaborate with data scientists, analysts, and business stakeholders to translate requirements into scalable technical solutions.
• Manage version control, branching, and CI/CD processes using Git.
• Drive adoption of engineering best practices, including modular code design, testing, and automation.

• Design, build, and maintain scalable data pipelines for both real-time and historical data processing.
• Develop and optimize databases, data warehouses, and ETL workflows handling large-scale time-series data.
• Process and manage high-volume data streams (thousands of records per second).
• Collaborate with Product, Customer Success, Installation, and Technical Support teams to ensure data quality and business alignment.
• Implement monitoring, alerting, and logging systems to ensure platform reliability.
• Support Data Science teams by improving data accessibility and facilitating model deployment.
• Troubleshoot and optimize data infrastructure performance.

• Safeguard the reliability, performance, and usability of end-to-end digital solutions
• Embed automated testing, AI-assisted workflows, and modern test tooling into our delivery lifecycle
• Define test strategy, build automation frameworks, and validate quality across modern web applications and mobile apps

Senior Data Engineer, Spark, Databricks

• Design, develop, and maintain data pipelines and ETL processes using Microsoft Azure services (e.g., Azure Data Factory, Azure Synapse, Azure Databricks, Azure Fabric).
• Utilize Azure data storage accounts for organizing and maintaining data pipeline outputs. (e.g., Azure Data Lake Storage Gen 2 & Azure Blob storage).
• Collaborate with data scientists, data analysts, data architects and other stakeholders to understand data requirements and deliver high-quality data solutions.
• Optimize data pipelines in the Azure environment for performance, scalability, and reliability.
• Ensure data quality and integrity through data validation techniques and frameworks.
• Develop and maintain documentation for data processes, configurations, and best practices.
• Monitor and troubleshoot data pipeline issues to ensure timely resolution.
• Stay current with industry trends and emerging technologies to ensure our data solutions remain cutting-edge.
• Manage the CI/CD process for deploying and maintaining data solutions.

Azure Data Engineer

• Design, build, and optimize the data infrastructure that powers reporting, analytics, and business intelligence capabilities
• Manage data synchronization across disparate database paradigms and enable stakeholders through modern BI tools
• Safeguard core production databases from intensive analytical workloads and unauthorized data extraction
• Write clean, maintainable, and typed code within the existing software ecosystem
• Develop backend services, scripts, and internal tools leveraging TypeScript and the MERN stack

• Build and optimize real-time streaming pipelines to ensure efficient routing of voice and chat session data into enterprise data platforms
• Integrate generative AI solutions with enterprise knowledge bases to ensure accurate and context-aware conversational agent performance
• Maintain and configure low-latency databases to ensure secure and reliable storage of session states and operational metadata
• Design system architectures and technical blueprints to ensure scalable, resilient, and high-performance solutions
• Delegate and manage engineering tasks to ensure effective team productivity and capability development
• Develop and execute testing processes to identify system issues and ensure quality and stability in deployed solutions
• Collaborate with stakeholders to align requirements, manage expectations, and ensure successful project delivery
• Oversee automated PII redaction workflows across voice and text streams to ensure full compliance with data privacy requirements prior to storage.

Senior Data Engineering Developer

• Transform, validate, and migrate critical data while ensuring accuracy, integrity, and a smooth transition.
• Support data conversion, validation, cleansing, and migration activities for client implementations.
• Ensure the accuracy, integrity, and successful transfer of data from legacy systems into new platforms.
• Provide exceptional support throughout the implementation process.
• Work with large datasets and collaborate with clients to ensure seamless transitions during system implementations.

Data Engineer jobs in Philippines

See all jobs on JobTailor

Senior Data Engineer

Senior Data Engineer

Data Engineer

Data Engineer

Senior Data Engineer, Snowflake

AI and Data Engineering Intern

Data Engineer

Senior Data Engineer

Senior Data Engineer, Spark, Databricks

Azure Data Engineer

Data Engineer

Senior Data Engineering Developer

Data Migration Specialist